Standard structural brain imaging protocols result in images that cannot resolve structures smaller than 1- 2mm in size. Achieving significantly higher resolution would be of fundamental clinical and neuroscientific value, as it would allow the in-vivo detection and analysis of cytoarchitectural features of the cortex, as well as substructures of brain regions such as the hippocampus, thalamus and amygdala. Unfortunately, such resolution is extremely difficult to obtain in-vivo, as the signal-to-noise ratio goes down with the third power of the linear dimension of each voxel. While some recent studies have pushed this limit to under 1A mm, this is at the cost of extremely long scan sessions and specialized imaging hardware, and even this is still a coarse resolution relative to what is required to visualize correlates of the cytoarchitecture with MRI. Here we take a different approach, and propose to image ex-vivo tissue samples, both blocks of tissue and whole hemispheres, in which exceedingly high-resolution is obtainable, on the order of lOOujns. In these images, many MR signatures of cytoarchitectural features are apparent, and hence they can be used for the construction of models including these cytoarchitectonically defined boundaries. For those features that are not distinguishable from the MR, we propose to perform histological analysis of the tissue, and use cross modal registration techniques to transfer the information from the histology back to the models. High dimensional mapping procedures are then proposed to map these models, obtained from ultra high-resolution imaging and histology, back to the more standard resolution in-vivo data to predict the probability of a given cytoarchitectural boundary occurring at each location in the in-vivo data. We focus on cortical areas in the medial temporal lobe as they are of great clinical relevance, as they are thought to be one of the earliest loci of Alzheimer's disease, and are critical to normal memory function. The ability to more accurately localize these cortical regions would be a critical step in the early diagnosis of AD, and in the assessment of the efficacy of potential clinical interventions.